Modern machinery analyzers such as vibration analyzers commonly oversample dynamic digital data at a sampling rate many times greater than a maximum frequency (FMAX) of data collection. Oversampled data is typically reduced to a desired frequency by either decimation filtering or peak value filtering. One or the other of these methods is commonly used to reduce oversampled data collected during a sampling interval to a single scalar value. With decimation filtering, the scalar value generally corresponds to machine vibration information. With peak value filtering, the scalar value generally corresponds to machine stress wave information. Peak value filtering is different from decimation filtering in that decimation filtering is a somewhat arbitrary rejection of oversampled data whereas peak value filtering is a somewhat selective rejection of oversampled data.
Oversampling and decimation filtering of a machine vibration signal to derive a scalar amplitude value for the machine vibration sensed during a sampling interval was first taught by Canada in U.S. Pat. No. 5,633,811. Peak value filtering (also referred to as “PeakVue™”, a trademark of Computational Systems, Inc.) of oversampled machine vibration data to derive a scalar PeakVue™ value representing stress wave information was first described by Robinson in U.S. Pat. No. 5,895,857. PeakVue™ is different from decimation in that decimation is a somewhat arbitrary rejection of oversampled data whereas PeakVue™ is a somewhat selective rejection of oversampled data and PeakVue™ is performed on a rectified signal. Leigh (U.S. Pat. No. 7,493,230) teaches a form of digital decimation using “an averager to determine the arithmetic mean or root mean square (RMS) of the rectified samples.”
Envelope techniques are different from decimation filtering and peak value filtering. Examples of envelope techniques include root mean squared (RMS), demodulation, short-time RMS (STRMS), Spectral Emission Energy (SEE™—a trademark of SKF Group), Spike Energy (also called gSE commonly cited by Entek IRD), and Shock Pulse Monitoring (SPM commonly cited by SPM Instruments). These envelope methods differ from peak value filtering and decimation filtering in that the envelope methods inherently have a knock-down smoothing or decay resulting in an envelope that does not include actual amplitudes of measured values.
Known techniques for trend analysis and compression of blocks of trend data, such as data collected using either on-line or walk-around condition monitoring devices, generally use a maximum value for each block, an average value for each block, and minimum value for each block. For example, each data point in a long-term trend may represent the minimum, maximum and average of 64 reported values. (See Reference Manual AMS™ Suite: Machinery Health™ Manager Online System Software Applications for the CSI 4500 Machinery Health™ Monitor, Part #97460.7, by Emerson Process Management (2007), page 3-53)
Prior art systems and methods incorporated by reference in their entirety herein include those described by Canada (U.S. Pat. No. 5,633,811), Robinson (U.S. Pat. No. 5,895,857 and U.S. Pat. No. 7,424,403), Piety (U.S. Pat. No. 5,965,819 and U.S. Pat. No. 5,943,634), Baldwin (US 2012/0041695), Leigh (U.S. Pat. No. 7,493,220) and Leigh (U.S. Pat. No. 8,219,361). Various embodiments of the present invention distinguish from all of these prior art techniques.
Table 1 below diagrammatically represents various applications where digital vibration signals are post-processed and decimated (columns labeled “post-processing” and “decimate”). Note that the table also represents analog signals, such as one from a piezoelectric accelerometer, which are typically transmitted to an analog preprocessing step (see “pre-process” column) before analog to digital conversion (see “digital signal” column). A digital signal is then post-processed and frequently decimated. Following the decimation step (or post-processing step if decimation is skipped), digital vibration signal information is analyzed, such as using AMS Machinery Health™ Manager software, and interpreted, such as by a vibration analyst using Machinery Health™ Manager software.
TABLE 1Process for interpreting analog sensor signal information.AnalogPre-DigitalPost-SignalProcessSignalProcessDecimateAnalyzeInterpretI.Analog AccelerometerYesII.Analog Vibration DataYesYesYesYesCollectorIII.Analog VibrationYesYesYesYesYesYesAnalyzerIV.Computer AnalyzerYesYesV.Vibration TransmitterYesYesYesYesYesYesYesVI.Digital AccelerometerYesYesYesYesYesVII.Digital VibrationYesYesAnalyzer
The steps outlined in Table 1 are most commonly performed using an analog accelerometer (I) in conjunction with an analog vibration data collector (II) or an analog vibration analyzer (III). Completion of analysis or further analysis of digital data streams from a data collector or from a vibration analyzer may be performed using a programmed computer analyzer.
For example, an analog piezoelectric accelerometer may be mounted on a machine to collect and translate mechanical vibrations into analog signals. That analog signal is typically transported in a cable as an analog voltage signal having a proportional value such as mV/g. The cable is also connected to a vibration analyzer, such as a CSI™ Model 2140 handheld analyzer or a CSI™ Model 6500 online analyzer. A handheld analyzer such as the CSI™ Model 2140 is often capable of analyzing and assisting an operator with interpreting vibration signal information. An online analyzer such as a CSI™ Model 6500 is often coupled to a personal computer programmed with vibration analysis software such as Machinery Health™ Manager software. The combined features of the online analyzer and the personal computer programmed with vibration analysis software enable an operator to analyze and interpret vibration signal information.
Vibration transmitters (V), such as a CSI™ Model 4100, and such as an analog transducer coupled with a CSI Model 9420 vibration transmitter, are designed and programmed to perform complete analysis and interpretation of analysis results. In order for self-contained, semi-autonomous devices like these to interpret results with no human analyst present, programmed logic firmware in a central processing unit typically supplants human interpretation of condition monitoring analyzed information.
A digital transducer such as a digital accelerometer (VI) typically includes an embedded analog accelerometer or MEMS sensor or other condition monitoring transducer. Pre-processing of analog signals, analog-to-digital conversion, post-processing of digital signals and decimation typically occur before digital waveforms or other digital data streams are transmitted by wired or wireless media to a receiving device, such as a computer analyzer (IV) or a programmed digital vibration analyzer (VII).